With rapid urbanization and population growth, major cities around the world, including Singapore, are facing the problem of traffic congestion. Therefore, in order to achieve sustainable growth, many cities are looking out for innovative solutions to improve the service quality of public transportation so as to make public transportation the preferred commuting choice for their residents. One of the extensively used public transport services in large metro cities is the railway system. However, there may be significant challenges in providing comfortable and good service quality in railway networks as travel demand increases. One approach for improving travel experience in public transport may be to keep the commuters waiting at the railway platforms or bus stops informed about crowd levels in the approaching train or bus. For railway services, the information on crowd levels may be provided for each carriage or train car in the train. This may allow the commuter to make an informed decision on whether to board the next approaching train or to choose the train carriage or car that is the least crowded. This type of service may therefore significantly improve the travel experience of the commuters. Sensors may be used to measure the crowd levels in the train cars. One approach may be to use onboard cameras in the train cars to measure the crowd level through image analysis. The measured crowd level for each train car may be communicated to over-head displays at the approaching station platform. The commuter will then have a choice to board the least crowded train car. The problem with such a method is that it only considers the current state or load of the train cars. The method cannot determine the future state when the train arrives at the next station and commuters alight from the train cars. This may be an important problem to be addressed as the passenger destinations may not be uniformly distributed amongst the various train cars. For example, a train car may have a large number of passengers alighting at the next station and therefore its load level will be low at the next station. Existing methods for measuring the current load of the train car, will likely inform the commuters waiting at the platform that the train car is full and unable to take in more passengers. As such, the commuters waiting to board the train will be misinformed, as in fact, the train car will have the capacity to take in more passengers at the next train station. Therefore, there is a need for a new method of predicting the passenger load in the train cars.